Robust Reinforcement Learning Objectives for Sequential Recommender Systems

arXiv:2305.18820v2 Announce Type: replace
Abstract: Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions. Recent research has also explored the integration of reinforcement learning (RL) into these models, in addition to generating superior user representations. By framing sequential recommendation as an RL problem with reward signals, we can develop recommender systems that incorporate direct user feedback in the form of rewards, enhancing personalization for users. Nonetheless, employing RL algorithms presents challenges, including …

abstract arxiv attention cs.ai cs.ir cs.lg integration interactions recommendation recommender systems reinforcement reinforcement learning research robust systems type